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Feature Engineering of Geohazard Susceptibility Analysis Based on the Random Forest Algorithm: Taking Tianshui City, Gansu Province, as an Example.

Authors :
Ling, Xiao
Zhu, Yueqin
Ming, Dongping
Chen, Yangyang
Zhang, Liang
Du, Tongyao
Source :
Remote Sensing; Nov2022, Vol. 14 Issue 22, p5658, 27p
Publication Year :
2022

Abstract

In this paper, Feature Engineering (FE) was applied to Landslide Susceptibility Mapping (LSM), while the most suitable conditioning feature dataset and analysis method were tested and analyzed. Tianshui city was taken as the study area, three types of geohazard (collapse, landslide, and unstable slopes) were used, while a total of twenty-three conditioning features were generated; two dimensionless methods (normalization and standardization) were tested afterward. Four Random-Forest-based (RF-based) feature selection methods using different indicators (Gini Impurity, GI; Out of Bag Accuracy, OOBA) were proposed and tested separately. The LSMs of four models were carried out under the guidance results of FE, namely Classification and Regression Tree (CART), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine for Classification (SVC). For feature enhancement, standardization had significant advantages over normalization. All RF-based methods were proven effective, lifting the AUC by 0.01~0.02. The RF model achieved the highest LSM accuracies, respectively, 0.949 (landslide), 0.957, and 0.949 (unstable slopes), improved by 0.008 (landslide), 0.005 (collapse), and 0.013 (unstable slopes). This proved that the FE helped to improve LSM and can help to decide the dominant conditioning factors for regional geohazards. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
22
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
160465327
Full Text :
https://doi.org/10.3390/rs14225658